Oscillations in the local field potential (LFP) of the brain are key signatures of neural information processing. Perturbing these oscillations at specific phases in order to alter neural information processing is an area of active research. Existing systems for phase-specific brain stimulation typically either do not offer real-time timing guarantees (desktop computer based systems) or require extensive programming of vendor-specific equipment. This work presents a real-time detection system architecture that is platform-agnostic and that scales to thousands of recording channels, validated using a proof-of-concept microcontroller-based implementation.
@article{arxiv.2009.07264,
title = {A Scalable Real-Time Architecture for Neural Oscillation Detection and Phase-Specific Stimulation},
author = {Christopher Thomas and Thilo Womelsdorf},
journal= {arXiv preprint arXiv:2009.07264},
year = {2021}
}
Comments
14 pages, 23 figures. To be submitted to IEEE Transactions on Signal Processing after further revision